Machining Fixture Layout Design for Milling Operation Using FEA, ANN and RSM

Vasundara, M.; Padmanaban, K.; Sabareeswaran, M.; RajGanesh, M.

doi:10.1016/j.proeng.2012.06.206

Cited by 22 publications

(10 citation statements)

References 19 publications

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“…The approach of genetic algorithm is coupled with nite element method which was running in batch mode to optimize xture layout and to gure out the objective function values for each group. Vasundara et al [6] dealt with forecasting the optimum xture layout for minimizing the maximum elastic deformation of the part during machining using both Arti cial Neural Networks (ANN) and Response Surface Methodology (RSM). The results of ANN and RSM are also compared by considering an example from the previous literature.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Optimization of Modular Fixture Layout by Minimizing Work-piece Deformation

Rashid

Khan

Qasim

et al. 2020

Preprint

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Machining fixtures are utilized to locate and restrain a workpiece during different manufacturing processes. The workpiece must be properly located and clamped in order to have it to be manufactured according to the prescribed dimensions and tolerance. The real motive of fixture design is to maximize locating accuracy and workpiece firmness while minimizing deformations. The purpose of this research work is to conduct a multi-objective optimization in order to minimize workpiece deflections due to clamping forces and optimized fixture layout by taking into consideration the boundary conditions and loads applied during a machining process. The locators are employed in a 3-2-1 fixture configuration. Then the empirical relations are used to calculate the machining forces and moments generated during drilling and milling processes and after that the workpiece is loaded to model those cutting forces. ANSYS parametric design language (APDL) code which made use of sub-approximation method is utilized to automatically optimize locator and clamp positions. Afterwards the clamping forces are being optimized using balancing force-moment method. Lastly, the maximum deformation of the workpiece against the optimum clamping forces is found by harmonic analysis.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Optimization of Modular Fixture Layout by Minimizing Work-piece Deformation

Rashid

Khan

Qasim

et al. 2020

Preprint

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“…Vasundara et al [11] performed the study of accuracy as the maximum value of the elastic deformation of the workpiece when it was fixed to different fixtures, where the preference was given to the construction, where the deformation is the least with other equal conditions. Chou and others [12] developed a method for determining the points of clamping the workpiece to ensure accuracy during machining.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental diagnostic research of fixture

Ivanov¹,

Dehtiarov²,

Denysenko³

et al. 2018

Diagnostyka

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In the article the experimental research of the flexible fixture is shown, which provides sufficient accessibility and allows to perform multiaxis machining of parts, e.g. of levers with one setup. The experimental studies compared with the results of numerical simulation that were previously obtained, confirmed that the proposed fixture design meets all accuracy parameters. The results of experimental studies on the determination of displacements have shown that the values of displacements, determined statically, in the proposed fixtures are smaller than the dedicated ones. The analysis of the data of determination of the frequencies of the proper oscillations in the machining of the levers from steel and cast iron has confirmed that the first critical frequency of the fixture significantly exceeds the similar parameter of dedicated fixture.

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“…Vasundara et al 11 applied backpropagation neural network (BPNN) to approximate the relationship between the positions of the fixturing elements and the part elastic deformation and compared the performance of artificial neural network (ANN) and RSM. Selvakumar et al 12 used BPNN to describe the function relationship between the positions of the locators and the maximum workpiece deformation and combined ANN-based algorithm with design of experiments (DOE) to optimize the machining fixture layout.…”

Section: Introductionmentioning

confidence: 99%

Optimal sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm

Wang

Yang

et al. 2016

Advances in Mechanical Engineering

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Considering that sheet metal part has the properties of thin wall, low rigidity, easy to deform, and difficult to locate, this article proposes a new approach to optimizing sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm. First, taking fixture locating layout as design variables based on the ''N-2-1'' locating principle, this article generates limited training and testing sample sets by Latin hypercube sampling and finite element analysis. Second, the radial basis function neural network prediction model with the description of the nonlinear mapping relationship between the fixture locating layout and the corresponding sheet metal deformation is constructed through learning from the training sample sets. Third, bat algorithm is applied to search the optimal layout of the ''N'' fixture locators for the minimum sheet metal deformation. Finally, two case studies are presented to demonstrate the optimization procedure and the effectiveness of the proposed method. KeywordsSheet metal part, fixture layout optimization, Latin hypercube sampling, radial basis function neural network, bat algorithm Date

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Machining Fixture Layout Design for Milling Operation Using FEA, ANN and RSM

Cited by 22 publications

References 19 publications

WITHDRAWN: Optimization of Modular Fixture Layout by Minimizing Work-piece Deformation

WITHDRAWN: Optimization of Modular Fixture Layout by Minimizing Work-piece Deformation

Experimental diagnostic research of fixture

Optimal sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm

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